Oliver Kirchkamp

Bayesian Methods

(here is information for the course on Bootstrap)
  • Introduction and Motivation
  • An example: Linear Regression
  • Finding posteriors
    • Analytic
      • Conjugate Priors
    • Metropolis-Hastings
    • Gibbs
    • Checking convergence
  • Applications:
    • Robust regression
    • Discrete Choice
    • Instrumental Variables
    • Errors in Variables
    • Interval regression
    • Hierarchical Models
  • Nonparametric Methods?
  • Identification
  • Model Comparison
  • Exercises
If you want to prepare for the lecture (or revise), you can have a look at the Handout (this is a preliminary version only, please expect changes during the next few weeks).
Download Handout
Here you will find the exam questions on Saturday, 11:00.
... can also be found in the appendix of the handout.
  • John K. Kruschke , Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. Academic Press, 2nd Edition, 2014.
  • Hoff, A First Course in Bayesian Statistical Methods. Springer, 2009.
To learn more about MCMC sampling you can read the following article:
For our practical examples (during the entire course) we will use the software environment R. I think that it is helpful to coordinate on one environment. R is free, it is very powerful, and it is popular in the field.
  • Documentation for R is provided throught the built in help. You also find support on the R Homepage. You might find the following useful:
    • The R Guide, Jason Owen (Easy to read, explains R with the help of examples from basic statistics)
    • Simple R, John Verzani (Explains R with the help of examples from basic statistics)
    • Einführung in R, Günther Sawitzki (In German. Rather compact introduction.)
    • Econometrics in R, Grant V. Farnsworth (The introduction to R is rather compact and pragmatic.)
    • An Introduction to R, W. N. Venables und D. M. Smith (The focus is more on R as a programming language)
    • The R language definition (Concentrates only on R as a programming language.)
    • On the JAGS Homepage you go to the files pages, then to Manuals, to find the JAGS user manual.
  • You can download R from the homepage of the R-project.
    Installing R with Microsoft Windows:
    Download and start the Installer. Install R on your local drive. Installing on a network drive or in the cloud (Dropbox, Onedrive,...) is possible but not recommended.
    Installing R with GNU-Linux:
    Follow the advice to install R for your distribution.
    Installing R with MacOS X:
    Here is a guide to install R with MacOS X.
  • In the lecture we use RStudio as a front end.
  • For the Bayesian parts we will use JAGS. It helps if you have installed R, RStudio, and JAGS on your computer when we start the course.
  • We will use the following packages: runjags, coda, boot, Ecdat, boot, bootstrap, survival, lattice, latticeExtra, lme4, lmtest, quantreg, xtable, plyr, reshape2, sampleSelection.

    If, e.g., the command library(Ecdat) generates an error message (Error in library(Ecdat): There is no package called 'Ecdat'), you have to install the package.

    Installing packages with Microsoft Windows:
    With RStudio: Use the tab “Install”. Otherwise: Start Rgui.exe and install packages from the menu Packages / Install Packages).
    Installing packages from GNU-Linux or MacOS X:
    From within R use the command install.packages("Ecdat"), e.g., to install the package Ecdat